Non-invasive detection of aflatoxin-contaminated figs using fluorescence and multispectral imaging

Abstract

Agricultural products are prone to aflatoxin (AF)-producing moulds (Aspergillus flavus, A. parasiticus) during harvesting, drying, processing and also storage. AF is a mycotoxin that may cause liver cancer when consumed in amounts higher than allowed limits. Figs, like other agricultural products, are mostly affected by AF-producing moulds and these moulds usually produce kojic acid together with AF. Kojic acid is a fluorescent compound and exhibiting bright greenish yellow fluorescence (BGYF) under ultraviolet (UV) light. Using this fluorescence property, fig-processing plants manually select and remove the BGYF+ figs to reduce the AF level of the processed figs. Although manual selection is based on subjective criteria and strongly depends on the expertise level of the workers, it is known as the most effective way of removing AF-contaminated samples. However, during manual selection, workers are exposed to UV radiation and this brings skin health problems. In this study, we individually investigated the figs to measure their fluorescence level, surface mould concentration and AF levels and noted a strong correlation between mould concentration and BGYF and AF, and BGYF and surface. In addition to a pairwise correlation, we proposed a machine-vision and machine-learning approach to detect the AF-contaminated figs using their multispectral images under UV light. The figs were classified in two different approaches considering their surface mould and AF level with error rates of 9.38% and 11.98%, respectively.